RPART Analysis
library("FRESA.CAD")
library(readxl)
library(igraph)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)
opo <- par(no.readonly = TRUE)
load("~/GitHub/BSWiMS/TADPOLE_BSWIMS_Results.RData")
op <- opo
Here we will diagnose ADAS13
RPARTml <- rpart::rpart(ADAS13~.,TADPOLECrossMRITrain)
prreg <- predictionStats_regression(cbind(TADPOLECrossMRITest$ADAS13,predict(RPARTml,TADPOLECrossMRITest)),"ADAS13")
ADAS13
pander::pander(prreg)
corci:
| cor | ||
|---|---|---|
| 0.615 | 0.567 | 0.659 |
biasci: -0.419, -0.990 and 0.153
RMSEci: 7.74, 7.35 and 8.16
spearmanci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.578 | 0.522 | 0.63 |
MAEci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 6.01 | 5.66 | 6.37 |
pearson:
| Test statistic | df | P value | Alternative hypothesis | cor |
|---|---|---|---|---|
| 20.7 | 702 | 1.49e-74 * * * | two.sided | 0.615 |
par(op)
TADPOLE_DX_TRAIN$DX <- as.factor(TADPOLE_DX_TRAIN$DX)
RPARTDXml <- rpart::rpart(DX~.,TADPOLE_DX_TRAIN)
prBin <- predictionStats_binary(cbind(TADPOLE_DX_TEST$DX,predict(RPARTDXml,TADPOLE_DX_TEST)[,2]),"MCI vs Dementia")
MCI vs Dementia
pander::pander(prBin$aucs)
| est | lower | upper |
|---|---|---|
| 0.699 | 0.644 | 0.754 |
pander::pander(prBin$accc)
| est | lower | upper |
|---|---|---|
| 0.702 | 0.654 | 0.748 |
pander::pander(prBin$berror)
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.408 | 0.354 | 0.461 |
pander::pander(prBin$sensitivity)
| est | lower | upper |
|---|---|---|
| 0.367 | 0.272 | 0.471 |
par(op)
TADPOLE_DX_NLDE_TRAIN$DX <- as.factor(TADPOLE_DX_NLDE_TRAIN$DX)
RPARTDXmlNLDE <- rpart::rpart(DX~.,TADPOLE_DX_NLDE_TRAIN)
prBin <- predictionStats_binary(cbind(TADPOLE_DX_NLDE_TEST$DX,predict(RPARTDXmlNLDE,TADPOLE_DX_NLDE_TEST)[,2]),"NL vs Dementia")
NL vs Dementia
pander::pander(prBin$aucs)
| est | lower | upper |
|---|---|---|
| 0.838 | 0.789 | 0.887 |
pander::pander(prBin$accc)
| est | lower | upper |
|---|---|---|
| 0.835 | 0.786 | 0.877 |
pander::pander(prBin$berror)
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.205 | 0.154 | 0.254 |
pander::pander(prBin$sensitivity)
| est | lower | upper |
|---|---|---|
| 0.663 | 0.561 | 0.756 |
par(op)
bConvml <- rpart::rpart(Surv(TimeToEvent,status)~.,TADPOLE_Conv_TRAIN)
ptestr <- predict(bConvml,TADPOLE_Conv_TEST)
ptestl <- log(ptestr)
boxplot(ptestl~TADPOLE_Conv_TEST$status)
boxplot(ptestr~TADPOLE_Conv_TEST$status)
perdsurv <- cbind(TADPOLE_Conv_TEST$TimeToEvent,
TADPOLE_Conv_TEST$status,
ptestl,
ptestr)
if (max(ptestl)>0 && min(ptestl)<0 )
{
prSurv <- predictionStats_survival(perdsurv,"MCI to AD Conversion")
pander::pander(prSurv$CIRisk)
pander::pander(prSurv$CILp)
pander::pander(prSurv$spearmanCI)
}
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.269 | 0.0864 | 0.452 |
prBin <- predictionStats_binary(cbind(TADPOLE_Conv_TEST$status,ptestl),"MCI to AD Conversion")
MCI to AD Conversion
pander::pander(prBin$aucs)
| est | lower | upper |
|---|---|---|
| 0.736 | 0.675 | 0.797 |
pander::pander(prBin$CM.analysis$tab)
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 66 | 60 | 126 |
| Test - | 32 | 115 | 147 |
| Total | 98 | 175 | 273 |
par(op)
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.257 | 0.0579 | 0.437 |
prBin <- predictionStats_binary(cbind(TADPOLE_Conv_TESTD$status,ptestl),"MCI to AD Conversion")
MCI to AD Conversion
pander::pander(prBin$aucs)
| est | lower | upper |
|---|---|---|
| 0.711 | 0.648 | 0.774 |
pander::pander(prBin$CM.analysis$tab)
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 82 | 77 | 159 |
| Test - | 16 | 98 | 114 |
| Total | 98 | 175 | 273 |
par(op)
.